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The Research on Image Enhancement of Low-quality Fingerprint Image

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The Research on Image Enhancement of Low-quality Fingerprint...

Particle Filter Data Fusion Enhancements for MEMS-IMU/GPS

20th Congress of the International Commission for Optics

作者： Bo Li Department of Physics College of Basic Sciences Donghua University Shanghai

Fingerprint Identification is one of the most reliable and popular personal identification methods. It can be divided into image acquisition, enhancement, feature extraction and matching steps. The en

关键词： fingerprint Fourier transform filtering Image enhancement low-quality feature extraction.

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Intelligent Information Management 2010年第7期2卷 417-421页

作者： Yafei Ren Xizhen Ke 不详

This research aims at enhancing the accuracy of navigation systems by integrating GPS and Mi-cro-Electro-Mechanical-System (MEMS) based inertial measurement units (IMU). Because of the conditions re-quired by the large number of restrictions on empirical data, a conventional Extended Kalman filtering (EKF) is limited to apply in navigation systems by integrating MEMS-IMU/GPS. In response to non-linear non-Gaussian dynamic models of the inertial sensors, the methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. Then Particle filtering (PF) can be used to data fusion of the inertial information and real-time updates from the GPS location and speed of information accurately. The experiments show that PF as opposed to EKF is more effective in raising MEMS-IMU/GPS navigation system’s data integration accuracy.

关键词： Micro-Electro-Mechanical-System Particle Filter Data Fusion Extended Kalman filtering

A Heuristic Reputation Based System to Detect Spam Activities in a Social Networking Platform, HRSSSNP

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Social Networking 2013年第1期2卷 42-45页

作者： Manoj Rameshchandra Thakur Sugata Sanyal Computer Science Department Veermata Jijabai Technological Institute Mumbai India Corporate Technology Office Tata Consultancy Services Mumbai India

The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [1]. Almost 33% of the crimes on the internet are initiated through a social networking website [1]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data set are used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.

关键词： Spam Social Graph Collaborative filtering Weighted Graph Localized Data-Set Trust Level

Two Section Algorithms For MRI Scanner

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Journal of Harbin Institute of Technology(New Series) 1999年第2期6卷 64-68页

作者：尚维陈骏姜俊峰

We develop two models for obtaining cross-secions at any angle through an object consisting of black-and-white line drawings and these two models both allow us to calculate the value of every pixel in the section, and thus a pictare of the section can be produces. We use a spherical co-ordinate system to describe the asction and vector p (φ,θ, ρ) can be uded to determine the section, if point p is in the section and the direction of vector p is the same as the orientation of the section. The method of smooth filtering is adopted in both models to filter sharp boundaries.Further-more, some other methods of filtering for special uses can be added to our model in the real practice, thus the model can be used in a larger range.We wrote a program using Visual Basic 5 .0 to implement our algorithm , created three data sets of simple three-dimensional object by functions, and produced fairly clear and accurate pictures of the slice of the three dimensioal array by several arbitrary planes in space. And we believe our model would significantly maintain the accurcy of pic-tures, even when the date from the object is more complex.

关键词： Spherical co-ordinate system projection plane smooth filtering weighted fitting

A Morphological Bandpass Filter for Signal Decomposition

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Journal of Harbin Institute of Technology(New Series) 1997年第3期4卷 39-42页

作者：赵春晖孙圣和

In this paper,the grayscale morphological opening operator in mathematicalmorphology is used to define a morphological bandpass filter,and its some properties ofthe filter are examined in detail,and a morphological shape decomposition algorithm isconstructed on the basis of this filter.It is testified that the morphological bandpass filter obeys a threshold-sum superposition law and It is implemented parallelly by means ofbinary morphological operators.

关键词： Mathematical morphology morphological filter nonlinear filtering image processing

Literature Review of Image Denoising Methods

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Computer Aided Drafting,Design and Manufacturing 2014年第1期24卷 18-22页

作者： LIU Qian YANG Xing-qiang LI Yunliang School of Compute Science and Technology Shandong University The Guard Department of Jinan Public Security Bureau

Image denoising is a fundamental and important task in image processing and computer vision fields. A lot of methods are proposed to reconstruct clean images from their noisy versions. These methods differ in both methodology and performance. On one hand, denoising methods can be classified into local and nonlocal methods. On the other hand, they can be marked as spatial and frequency domain methods. Sparse coding and low-rank are two popular techniques for denoising recently. This paper summarizes existing techniques and provides several promising directions for further studying in the future.

关键词： filtering wavelet nonlocal self-similarity sparse representation low-rank

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Inferno on Jupiter

当代外语研究 1994年第12期 7-9页

作者：诸葛勤

The initial sketchy reports began filtering into the U. S. by E-maillate Saturday afternoon. First a Spanish observatory announced that it hadspotted a plume of gas billowing up from the edge of Jupiter. Then

关键词： Jupiter afternoon Spanish plenty filtering really stake silent planet perhaps

CLEARING ELECTRON MICROSCOPIC IMAGES OF PROTEIN CRYSTALS BY OPTICAL METHOD

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Chinese Science Bulletin 1982年第1期27卷 83-85页

作者：李钦张树政郑师海董碧珍潘少华 Institute of Microbiology Academia Sinica Institute of Phyisics Academia Sinica

Electron micrographs with a periodic structure can be cleared by utilizing optical filtering techniques. The principle of the method is first to make an optical diffraction pattern, using a transparent electron mirograph as the diffraction grating. This pattern of diffraction spots is then carefully examined, and all the possible forms

关键词： utilizing spots grating Electron carefully filtered filtering transparent suspension 加略

Analysis of Optimal Conditions for Two-stage Kalman Estimator

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Journal of Harbin Institute of Technology(New Series) 1997年第3期4卷 106-109页

作者：周露吴瑶华黄文虎闻新

The optimal conditions for two-stage Kalman estimator with random bias of anARMA model is considered in this paper.First,the optimal augmented state Kalman fil-ter and the two-stage Kalman estimator are given.Second,under an algebraic constraint,the equivalence between the two-stage Kalman estimator and the optimal augmented stateKalman filter is proved.Finally,because the given algebraic constraint are restrictive inpractice,the results thus obtained implies that two-stage Kalman estimator is suboptimal.

关键词： Kalman filter estimator of state optimal filtering two-stage kalman estimator ARM A model random bias